Benchmarking state-of-the-art visual saliency models for image quality assessment
Wei Zhang, Yi Tian, Xiaojie Zha, Hantao Liu
Abstract
A significant current research trend in image quality assessment is to investigate the added value of visual attention aspects. Previous approaches mainly focused on adopting a specific saliency model to improve a specific image quality metric (IQM). It is still not known yet which of the existing saliency models is generally applicable in IQMs; which of the IQMs can profit most/least from the addition of saliency; and how this improvement depends on the saliency model used and the IQM targeted. In this paper, a large-scale benchmark study is conducted to assess the capabilities and limitations of the state-of-the-art saliency models in the context of IQMs. The study provides guidance for the application of saliency models in IQMs, in terms of the effect of saliency model dependency, IQM dependency, and image distortion dependency.
BibTeX
@inproceedings{icassp2016_benchmarkingstat,
title = {Benchmarking state-of-the-art visual saliency models for image quality assessment},
author = {Wei Zhang and Yi Tian and Xiaojie Zha and Hantao Liu},
booktitle = {ICASSP 2016},
year = {2016}
}